On the Statistical Delay Performance of Large-Scale IoT Networks

Abstract

Large-scale Internet of things (IoT) networks have shown great advantages and attracted much attention in 5G networks due to its high-speed Internet connectivity, high data rate, ultra reliability and low latency. However, the diversity of ubiquitous IoT devices and complex transmission environment lead to the difficulty of theoretical analysis of delay performance. In order to capture the characteristics of large IoT networks, a novel integrated analysis framework is developed to build the performance model and analyze the statistical delay performance in this paper. Specifically, we model the spatial distribution of IoT devices with heterogeneous Poisson point processes. With the Laplace transform of signal-to-interference-plus-noise ratio, the service capability of the wireless channel with multiple interferes is determined with a mathematical stochastic-network-calculus aided approach in the exponential domain for Poisson bipolar and cellular networks. Then, the upper bound of delay is achieved by the Mellin transform of the service incremental process. We extend our work to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -tier heterogeneous network and apply the analysis results to a practical two-tier cognitive radio IoT network. Simulation results validate the theoretical analysis of delay performance with various network parameters.